HIERARCHICAL CLUSTER BASED UNDER

WATER COMMUNICATION

1M.Thivya ,2G. Srinidhi ,3K.Vishnupriya,4M.Seenivasan,5K.Monisha

1234UGScholors ,Mangayarkarasi college of engineering ,Madurai

5Assistance Professor ,Mangayarkarasi College Of Engineering ,Madurai.

1.thivyathivya10@gmail.com,2.jackfletcherflectcher@gmail.com,3.vishnu.kathir1302@gmail.co

m,4.seenijilla00@gmail.com,5.kcmonisha@gmail.com

ABSTRACT

Water Pipeline Monitoring Systems

have emerged as a reliable solution to

maintain the integrity of the water

distribution infrastructure. Various emerging

technologies such as the Internet of Things,

Physical Cyber Systems, and machineto-

machine networks are efficiently deployed

to build a Structural Health Monitoring of

pipeline and invoke the deployment of the

Industrial Wireless Sensor Networks

(IWSN) technology. Efficient energy

consumption is imperatively required to

maintain the continuity of the network and

to allow an adequate interconnection

between sensor nodes deployed in the harsh

environment. In this context, to maximize

the Lifetime of the WSN underwater

Distribution system domain is a primordial

objective to ensure its permanently working

and to enable a promising solution for

hydraulic damage detection according to

diverse performance metrics In this context,

the data aggregation techniques are well-

designed and various smart algorithms are

developed to reduce the quantity of

transmitted data and to minimize the energy

consumption. In this paper, we combine

between data aggregation and clustering

algorithm in order to improve the WSN

Lifetime.

INTRODUCTION

Recently, advancements in Wireless sensor

networks(WSNs) domain make possible to

deploy them efficiently inmany real-life

applications, such as environment, health

and

water pipeline monitoring. The main

problem in WSNs is howto efficiently

collect and deliver the sensed data.

Wirelesssensor nodes are generally small in

size with limited

communication range that depends on the

transmission power.In addition, sensor

nodes normally operate while relying on

asmall capacity battery. Considering the

difficulty of replacingthe battery after

deploying the sensor nodes, it is necessary

tomanage their energy consumption to

achieve the maximum

operation time for WSNs. In this paper, we

focus on WSNsthat are used to monitor

water pipelines. In fact, we study thelinear

sensor placement problem. Many studies in

literatureaddressed the linear sensor

deployment problem and proposeseveral

solutions that could be applied to many

domainapplications In this paper, we focus

on WSNsthat are used to monitor water

pipelines. In fact, we study thelinear sensor

placement problem. Many studies in

literatureaddressed the linear sensor

deployment problem and proposeseveral

solutions that could be applied to many

domainapplications

II. RELATED WORK

The damage of water pipeline is very critical

and hasmalicious effects in the life of human

being. Nevertheless,installing, upgrading,

and replacing infrastructure of water

pipeline requires large investments of

money and time.Therefore, developing a

real time system to monitor leak inwater

pipelines and transfer the measurements

collected from

different distributed sensors is very

important [1]. Sensornodes must have the

ability to detect events and transmit

theinformation. Therefore, the deployment

of nodes mostly

affects the function of WSNs and in

consequence theperformance of WSNs.

Thus, the development of a sensor

deployment scheme has become an

important research issue inrecent years.

There are several works proposed to solve

the

problem of sensor deployment in many

application domains.The original locations

of deployed sensors can be changed due

to many conditions such as new nodes join

the networks orprevious nodes leave the

networks in case of die out.Therefore,

perfect node deployment is a very

challengingproblem that has been proven as

NP-Hard for the majority ofsensor

deployment approaches. The first objective

of applyinga deployment algorithm is to

minimize the number of sensorsneeded

while respecting the constraint of having a

maximumcoverage and connectivity

throughout the region. As firstsolution, there

is some works in literature that studied

theproblem of sensor deployment as an (art

gallery) difficulty [3].A randomized

algorithm is proposed in [4] to solve the art

gallery problem by finding places for sensor

nodes.Nevertheless, the assumptions in the

art gallery problem, doesnot hold for WSNs

in which sensor nodes have limited sensing

ranges [5].

THE ALSN MODEL

Our proposed model helps reduce the impact

of energylosses by minimizing its use. The

overall framework relieson short range

communication and autonomous underwater

vehicles (AUVs) that travel across the LSN

to collect data.As a result, no multihop

communication is needed and nodes

need only transmit data within a very short

distance. Fig. 1and Fig. 2 show the ALSN

model and data exchange process

respectively. As shown in Fig. 1, the model

contains threetypes of nodes:

Sensor Node (SN): SNs perform the sensing

operations.The type of sensing data that is

collected depends onthe application, content

of the pipeline, and objectivesof the

monitoring and control process.

AUV: The AUV is responsible for collecting

data from theSNs and delivering it to the

surface sink. It is a more capablenode. More

specifically, compared to the SN node, it

has more processing power, more memory

and storage capacity,more energy, and a

longer communication rangeSurface Sink:

Surface sinks are placed at both ends of

the ALSN network. As indicated earlier, the

AUV movesalong the ALSN and delivers its

data to the surface sink.Then, the surface

sink uploads the collected SN data tothe

network control center (NCC) using the

networkingprotocols that are available in the

local geographicarea.

Fig. 1: The ALSN model.

Fig. 2: Exchange of data when the AUV is

within range of

the SN.

Considering the energy constraints in

wireless sensor networks the proposed DA is

designed to provide energy-efficient data

aggregation together with secure data

communication.DA protocol consists of a

number of algorithms and processes. Sensor

nodes implement the following processes.

Sensing data from the environment.

Defining intervals from threshold values set

for the environment parameters. Assigning

critical values for intervals using pattern

seed from cluster-head. Generating the

lookup table. Generating pattem codes using

pattern generation algorithm. , Sending

pattern codes to cluster-heads. Receiving

send-requestslACK from the cluster-head

Sending actual data to cluster-heads.

Cluster-head performs the following

processes.

Broadcasting the pattern seed for each time

interval.

Receiving pattern codes from sensor nodes.

Forming the selected-set of pattern codes

using the pattern comparison algorithm.

Requesting selected sensor nodes to send

actual data.

The pattern generation and comparison

algorithms as well as an example for pattern

generation are presented in the sequel.

Pattern Generation Sensor nodes receive the

secret pattern seed from the cluster head.

The interval values for the data are defined,

based on the given threshold values set for

each environment parameter. The number of

threshold values and the variation of

intervals may depend on the user

requirement and the precision defined for

the given environment in which the network

is deployed. The algorithm then computes

the critical values for each interval using the

pattem seed to generate the lookup table,

where the' pattem seed is a random number

generated and broadcasted by the cluster-

head. This pattern seed is changed at regular

time intervals. In ESPDA, the pattern

generation algorithm (PG) which is executed

on all sensoc nodes uses the pattern seed to

generate pattern codes. Before sending the

actual data, the sensor nodes send the pattem

codes to the cluster-head. These patterns are

analyzed by the pattern comparison

algorithm at the cluster-head to prevent

redundant data being transmitted Sensor

nodes sends the set of unique data (without

redundancy) to the cluster-head which is

transmitted to the base station.

Pattern Comparison The cluster-head also

has equal responsibility as the sensor nodes

in data aggregation. It sends the pattern seed

periodically to all active sensor nodes to

maintain the confidentiality of the pattem

codes. After receiving pattern codes from

the sensor nodes for a time period T, the

entire set of codes is classified based on

redundancy. Unique patterns are then moved

to the ‘selected-set’ of codes. The time

period T varies based on the environment

where the sensor network is deployed. The

sensors nodes that correspond to the unique

panem set (‘selected-set’) are then requested

to transmit the actual data

NS2 IMPLEMENTATION

The proposed protocol has been simulated

using Network Simulation 2 (NS-2) in

version 3.5. The performance of the

proposed protocol has been evaluated

through comparing its QoS metrics with

some other existing methods such in terms

of packet delivery ratio, packet delivery

delay and control overhead.

Fig Energy analysis

Fig packet delivery ratio

Fig speed versus delivery ratio

CONCLUSION

A data aggregation clustering algorithm

combined with DataElimination

Redundancy technique was detailed and

implemented.The algorithm is based on two

main phases. Firstly,the divide the WSN into

anumber of clusters and the define the best

data aggregation that ensures the minimum

of Energy consumption andthe shortest

distance between sensor members and CH.

Efficient dataaggregation allowing the

redundancy elimination at the clusterand

sensor node level improves more the results

and reducesthe energy consumption.

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